





import gc
import random
import unittest

import numpy as np
import torch

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()







class      lowerCamelCase(						__snake_case			, unittest.TestCase	):
     '''simple docstring'''


     __magic_name__   =			KandinskyVaaControlnetPipeline
     __magic_name__   =			['image_embeds', 'negative_image_embeds', 'hint']
     __magic_name__   =			['image_embeds', 'negative_image_embeds', 'hint']
     __magic_name__   =			[
         'generator',
         'height',
         'width',
         'latents',
         'guidance_scale',
         'num_inference_steps',
         'return_dict',
         'guidance_scale',
         'num_images_per_prompt',
         'output_type',
         'return_dict',
     ]
     __magic_name__   =			False




     @property
     def        lowerCAmelCase__		(    self							):
         return 32




     @property
     def        lowerCAmelCase__		(    self							):
         return 32




     @property
     def        lowerCAmelCase__		(    self							):
         return self.time_input_dim




     @property
     def        lowerCAmelCase__		(    self							):
         return self.time_input_dim * 4




     @property
     def        lowerCAmelCase__		(    self							):
         return 100




     @property
     def        lowerCAmelCase__		(    self							):
         torch.manual_seed(0							)

         _A     =							{
             'in_channels': 8,
             # Out channels is double in channels because predicts mean and variance
             'out_channels': 8,
             'addition_embed_type': 'image_hint',
             'down_block_types': ('ResnetDownsampleBlock2D', 'SimpleCrossAttnDownBlock2D'),
             'up_block_types': ('SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'),
             'mid_block_type': 'UNetMidBlock2DSimpleCrossAttn',
             'block_out_channels': (self.block_out_channels_a, self.block_out_channels_a * 2),
             'layers_per_block': 1,
             'encoder_hid_dim': self.text_embedder_hidden_size,
             'encoder_hid_dim_type': 'image_proj',
             'cross_attention_dim': self.cross_attention_dim,
             'attention_head_dim': 4,
             'resnet_time_scale_shift': 'scale_shift',
             'class_embed_type': None,
         }

         _A     =							UNetaDConditionModel(**snake_case_							)
         return model




     @property
     def        lowerCAmelCase__		(    self							):
         return {
             "block_out_channels": [32, 32, 64, 64],
             "down_block_types": [
                 "DownEncoderBlock2D",
                 "DownEncoderBlock2D",
                 "DownEncoderBlock2D",
                 "AttnDownEncoderBlock2D",
             ],
             "in_channels": 3,
             "latent_channels": 4,
             "layers_per_block": 1,
             "norm_num_groups": 8,
             "norm_type": "spatial",
             "num_vq_embeddings": 12,
             "out_channels": 3,
             "up_block_types": ["AttnUpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"],
             "vq_embed_dim": 4,
         }




     @property
     def        lowerCAmelCase__		(    self							):
         torch.manual_seed(0							)
         _A     =							VQModel(**self.dummy_movq_kwargs							)
         return model




     def        lowerCAmelCase__		(    self							):
         _A     =							self.dummy_unet
         _A     =							self.dummy_movq

         _A     =							DDIMScheduler(
             num_train_timesteps=1000				,     beta_schedule='linear'				,     beta_start=0.0_0085				,     beta_end=0.012				,     clip_sample=snake_case_				,     set_alpha_to_one=snake_case_				,     steps_offset=1				,     prediction_type='epsilon'				,     thresholding=snake_case_				,     )

         _A     =							{
             'unet': unet,
             'scheduler': scheduler,
             'movq': movq,
         }
         return components




     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_=0							):
         _A     =							floats_tensor((1, self.text_embedder_hidden_size)				,     rng=random.Random(snake_case_							)							).to(snake_case_							)
         _A     =							floats_tensor((1, self.text_embedder_hidden_size)				,     rng=random.Random(seed + 1							)							).to(
             snake_case_							)

         # create hint
         _A     =							floats_tensor((1, 3, 64, 64)				,     rng=random.Random(snake_case_							)							).to(snake_case_							)

         if str(snake_case_							).startswith('mps'							):
             _A     =							torch.manual_seed(snake_case_							)
         else:
             _A     =							torch.Generator(device=snake_case_							).manual_seed(snake_case_							)
         _A     =							{
             'image_embeds': image_embeds,
             'negative_image_embeds': negative_image_embeds,
             'hint': hint,
             'generator': generator,
             'height': 64,
             'width': 64,
             'guidance_scale': 4.0,
             'num_inference_steps': 2,
             'output_type': 'np',
         }
         return inputs




     def        lowerCAmelCase__		(    self							):
         _A     =							'cpu'

         _A     =							self.get_dummy_components()

         _A     =							self.pipeline_class(**snake_case_							)
         _A     =							pipe.to(snake_case_							)

         pipe.set_progress_bar_config(disable=snake_case_							)

         _A     =							pipe(**self.get_dummy_inputs(snake_case_							)							)
         _A     =							output.images

         _A     =							pipe(
             **self.get_dummy_inputs(snake_case_							)				,     return_dict=snake_case_				,     )[0]

         _A     =							image[0, -3:, -3:, -1]
         _A     =							image_from_tuple[0, -3:, -3:, -1]

         assert image.shape == (1, 64, 64, 3)

         _A     =							np.array(
             [0.695_9826, 0.86_8279, 0.755_8092, 0.6876_9467, 0.8580_5804, 0.6597_7496, 0.4488_5302, 0.595_9111, 0.425_1595]							)

         assert (
             np.abs(image_slice.flatten() - expected_slice							).max() < 1E-2
         ), F" expected_slice {expected_slice}, but got {image_slice.flatten()}"

         assert (
             np.abs(image_from_tuple_slice.flatten() - expected_slice							).max() < 1E-2
         ), F" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"







@slow
@require_torch_gpu
class      lowerCamelCase(						unittest.TestCase	):
     '''simple docstring'''


     def        lowerCAmelCase__		(    self							):
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()




     def        lowerCAmelCase__		(    self							):
         _A     =							load_numpy(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
             '/kandinskyv22/kandinskyv22_controlnet_robotcat_fp16.npy'							)

         _A     =							load_image(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
             '/kandinskyv22/hint_image_cat.png'							)
         _A     =							torch.from_numpy(np.array(snake_case_							)							).float() / 255.0
         _A     =							hint.permute(2				,     0				,     1							).unsqueeze(0							)

         _A     =							KandinskyVaaPriorPipeline.from_pretrained(
             'kandinsky-community/kandinsky-2-2-prior'				,     torch_dtype=torch.floataa							)
         pipe_prior.to(snake_case_							)

         _A     =							KandinskyVaaControlnetPipeline.from_pretrained(
             'kandinsky-community/kandinsky-2-2-controlnet-depth'				,     torch_dtype=torch.floataa							)
         _A     =							pipeline.to(snake_case_							)
         pipeline.set_progress_bar_config(disable=snake_case_							)

         _A     =							'A robot, 4k photo'

         _A     =							torch.Generator(device='cuda'							).manual_seed(0							)
         _A,			_A     =							pipe_prior(
             snake_case_				,     generator=snake_case_				,     num_inference_steps=5				,     negative_prompt=''				,     ).to_tuple()

         _A     =							torch.Generator(device='cuda'							).manual_seed(0							)
         _A     =							pipeline(
             image_embeds=snake_case_				,     negative_image_embeds=snake_case_				,     hint=snake_case_				,     generator=snake_case_				,     num_inference_steps=100				,     output_type='np'				,     )

         _A     =							output.images[0]

         assert image.shape == (512, 512, 3)

         assert_mean_pixel_difference(snake_case_				,     snake_case_							)







import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
		from .tokenization_fnet import FNetTokenizer
else:
		lowercase    :   List[Any]						=			None

lowercase    :   Tuple						=			logging.get_logger(__name__)
lowercase    :   Optional[Any]						=			{'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

lowercase    :   str						=			{
    'vocab_file': {
        'google/fnet-base': 'https://huggingface.co/google/fnet-base/resolve/main/spiece.model',
        'google/fnet-large': 'https://huggingface.co/google/fnet-large/resolve/main/spiece.model',
    },
    'tokenizer_file': {
        'google/fnet-base': 'https://huggingface.co/google/fnet-base/resolve/main/tokenizer.json',
        'google/fnet-large': 'https://huggingface.co/google/fnet-large/resolve/main/tokenizer.json',
    },
}

lowercase    :   int						=			{
    'google/fnet-base': 512,
    'google/fnet-large': 512,
}

lowercase    :   str						=			'▁'




class 					lowerCamelCase__				(							__lowercase):




	'''simple docstring'''




	_A								=	VOCAB_FILES_NAMES
	_A								=	PRETRAINED_VOCAB_FILES_MAP
	_A								=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_A								=	['input_ids', 'token_type_ids']
	_A								=	FNetTokenizer







	def __init__(   self						:Optional[int]	,  a						:Union[str, Any]=None	,  a						:Optional[int]=None	,  a						:str=False	,  a						:Tuple=True	,  a						:Optional[Any]=True	,  a						:Optional[int]="<unk>"	,  a						:List[str]="[SEP]"	,  a						:Optional[Any]="<pad>"	,  a						:int="[CLS]"	,  a						:Dict="[MASK]"	,  **a						:Optional[int]	,  )						->					List[str]:
								# Mask token behave like a normal word, i.e. include the space before it and
								# is included in the raw text, there should be a match in a non-normalized sentence.
								__UpperCamelCase      :  List[Any]       =   (
								    AddedToken(a	,  lstrip=a	,  rstrip=a	,  normalized=a		)
								    if isinstance(a	,  a		)
								    else mask_token
								)

								super().__init__(
								    a	,  tokenizer_file=a	,  do_lower_case=a	,  remove_space=a	,  keep_accents=a	,  unk_token=a	,  sep_token=a	,  pad_token=a	,  cls_token=a	,  mask_token=a	,  **a	,  )

								__UpperCamelCase      :  List[Any]       =   do_lower_case
								__UpperCamelCase      :  str       =   remove_space
								__UpperCamelCase      :  Dict       =   keep_accents
								__UpperCamelCase      :  Union[str, Any]       =   vocab_file
								__UpperCamelCase      :  List[str]       =   False if not self.vocab_file else True







	def   _lowerCamelCase							(   self						:Dict	,  a						:List[int]	,  a						:Optional[List[int]] = None		)						->					List[int]:
								__UpperCamelCase      :  List[str]       =   [self.sep_token_id]
								__UpperCamelCase      :  List[str]       =   [self.cls_token_id]
								if token_ids_a is None:
															return cls + token_ids_a + sep
								return cls + token_ids_a + sep + token_ids_a + sep







	def   _lowerCamelCase							(   self						:Union[str, Any]	,  a						:List[int]	,  a						:Optional[List[int]] = None		)						->					List[int]:
								__UpperCamelCase      :  List[Any]       =   [self.sep_token_id]
								__UpperCamelCase      :  List[Any]       =   [self.cls_token_id]

								if token_ids_a is None:
															return len(cls + token_ids_a + sep		) * [0]
								return len(cls + token_ids_a + sep		) * [0] + len(token_ids_a + sep		) * [1]







	def   _lowerCamelCase							(   self						:int	,  a						:str	,  a						:Optional[str] = None		)						->					Tuple[str]:
								if not os.path.isdir(a		):
															logger.error(f'Vocabulary path ({save_directory}) should be a directory'		)
															return
								__UpperCamelCase      :  str       =   os.path.join(
								    a	,  (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)

								if os.path.abspath(self.vocab_file		) != os.path.abspath(a		):
															copyfile(self.vocab_file	,  a		)

								return (out_vocab_file,)